Eph/Ephrin memhrane proteins: A mammalian expression vector pTIg-BOS-Fc allowing rapid protein purification

There is an urgent need for high purity, single chain, fully functional Eph/ephrin membrane proteins. This report outlines the pTIg-BOS-Fc vector and purification approach resulting in rapid increased production of fully functional single chain extracellular proteins that were isolated with high purity and used in structure-function analysis and pre-clinical studies.

Real-Time Gene-Expression Extraction with Fast Classification (FC) Networks

Fast Classification (FC) networks were inspired by a biologically plausible mechanism for short term memory where learning occurs instantaneously. Both weights and the topology for an FC network are mapped directly from the training samples by using a prescriptive training scheme. Only two presentations of the training data are required to train an FC network. Compared with iterative learning algorithms such as Back-propagation (which may require many hundreds of presentations of the training data), the training of FC networks is extremely fast and learning convergence is always guaranteed. Thus FC networks may be suitable for applications where real-time classification is needed. In this paper, the FC networks are applied for the real-time extraction of gene expressions for Chlamydia microarray data. Both the classification performance and learning time of the FC networks are compared with the Multi-Layer Proceptron (MLP) networks and support-vector-machines (SVM) in the same classification task. The FC networks are shown to have extremely fast learning time and comparable classification accuracy.